GB2563112A - An apparatus and method - Google Patents

Abstract

There is disclosed an apparatus and a method for calculating a biomarker value. The apparatus includes an analysis module configured to obtain a heartbeat trace of a subject; determine at least one parameter describing the heartbeat trace, and; calculate at least one output biomarker value using at least one Artificial Neural Network ("ANN"). An input of the ANN includes the at least one parameter. An output of the ANN includes the at least one output biomarker value. The ANN may involve a plurality of input 22A, output 23A and hidden neurons 24A. The heartbeat trace may be determined from a video of the subject wherein intensity within a target region of the video is used. The biomarker may be blood glucose level, blood pressure, core or skin temperature, blood gas saturation, histamine levels amongst other examples. The heartbeat trace may be combined from multiple individual heartbeat profiles into a super pulse before input into the ANN.

Description

This application claims priority from GB1708591.1 filed 30 May 2017, the contents and elements of which are herein incorporated by reference for all purposes.

Field of the invention

The present invention relates to an apparatus and method for calculating a biomarker value, and more particularly to an apparatus and method for calculating a biomarker value using an artificial neural network.

Background

Diabetes mellitus (DM), of which type 2 diabetes mellitus (T2DM) represents 85-95% of cases of diabetes in adults, has increased dramatically to pandemic proportions. T2DM affected 450 million adults in 2014, approximately 8.5% of the world population and is predicted to rise 11.6% by 2025.(Press 2016)

Increased insulin resistance is a biomarker of T2DM subjects. Impaired glucose tolerance marks the progression between normal glucose tolerance and diabetes (Lillioja, Mott et al. 1993), (Reaven 1988).

Treatment goals for T2DM are to eliminate symptoms, to maintain a normal quality of life and work capability, to prevent the occurrence of acute metabolic disorder, and to prevent and delay the occurrence and development of chronic complications. Therefore, diabetes treatment is lifelong process. In addition to insulin and hypoglycaemic drugs, diet is the basis for the treatment of diabetes. In all treatments, blood glucose level monitoring is important.

Most of methods for blood glucose level measurement are invasive. Sample blood from patients is taken and the blood glucose level is measured, typically by glucose oxidase (GOx) method. Venous whole blood, plasma or serum glucose is tested in hospitals. Capillary whole blood glucose can be checked on a portable device operated by a patient. To maintain the glucose at a desired level, the blood glucose has to be tested several times per day, including at least before and after the three meals and before bed. If a patient suffers nocturnal hypoglycaemia, additional testing is needed. All of these methods accurately obtain a blood glucose level, but there are problems and limitations. First, it is very painful to take blood samples multiple times per day. Second, it is costly to use glucose oxidase reagents or test strips in hospital or at home, which presents a significant financial burden to patients. In addition, self-testing at home may lead to blood contamination and bacterial infection.

Therefore development of a non-invasive type of blood glucose monitoring technologies and devices has been a long-term goal of many research institutions and companies. It is an object of the present invention to provide an improved apparatus and method for non-invasive blood glucose monitoring.

Summary of the invention

According to a first aspect of the invention, an apparatus for calculating a biomarker value is provided, comprising: an analysis module configured to: obtain a heartbeat trace of a subject; determine at least one parameter describing the heartbeat trace; calculate at least one output biomarker value using at least one Artificial Neural Network (“ANN”); wherein an input of the ANN includes the at least one parameter, and an output of the ANN includes the at least one output biomarker value.

In the context of the present invention, the apparatus may be a mobile device. The mobile device may be a mobile phone, a so-called smart watch, a wearable device such as glasses or contact lens, a computer peripheral (which may be configured for wired/wireless connection to a computer), a tablet, a computer or any other suitable mobile device. The mobile device may be a substantially dedicated biomarker measurement device. For example, the mobile device may be a blood glucose monitor, i.e. a dedicated device for monitoring blood glucose level.

The device may be capable of analysing video, either obtained locally, or supplied as data to the mobile device. Furthermore, the mobile device may include the means by which to obtain the video. For example, the mobile device may include a camera for obtaining video.

The mobile device may also be capable of recording the biomarker values obtained according to the present invention. The biomarkers may be stored on the device and/or sent to a remote location. In other words the device may be used to track and record biological and physiological parameters.

The mobile device may be configured for external data connection. For example, the mobile device may have a network connection means, for example a wifi or “Bluetooth” wireless connection. The mobile device may be configured to send the biomarker measurements to a remote device, for example a remote server, or another mobile device or computer belonging to the user.

Advantageously, the ANN includes an input layer having a plurality of input neurons, where the number of input neurons is equal to the number of parameters.

Conveniently, the ANN includes an output layer having at least one output neuron, where the number of output neurons is equal to the number of output biomarker values.

Preferably, obtaining the heartbeat trace includes deriving the heartbeat trace from a video of the subject.

Advantageously, obtaining the heartbeat trace includes measuring an intensity within a target region of each frame of the video, the intensity as a function of time forming the heartbeat trace.

Conveniently, obtaining the heartbeat trace includes: performing a Fourier transform of the heartbeat trace to identify the heartbeat frequency.

Preferably, obtaining the heartbeat trace includes measuring a noise signal in the heartbeat trace, and removing the noise signal from the heartbeat trace.

Advantageously, the obtaining the heartbeat trace includes combining a plurality of individual heartbeat profiles in the heartbeat trace to form a combined heartbeat profile.

Conveniently, a plurality of individual heartbeat profiles are temporally aligned using a feature of the plurality of the individual heartbeat profiles.

Preferably, the ANN includes a primary ANN and at least one precursor ANN.

Advantageously, at least one of the input numerical values to the primary ANN is an output from one of the at least one precursor ANN.

Conveniently, the input of the ANN includes at least one extrinsic value for the subject.

Preferably, determining at least one parameter describing the heartbeat trace includes determining a functional form of the super pulse, and wherein the input of the ANN includes at least one parameter of the functional form.

Advantageously, the functional form includes a plurality of Gaussian functions.

According to a second aspect of the present invention, a method of calculating a biomarker value is provided, comprising: obtaining a heartbeat trace of a subject; determining at least one parameter describing the heartbeat trace; calculating at least one output biomarker value using at least one Artificial Neural Network (“ANN”); wherein an input of the ANN includes the 3 at least one parameter, and an output of the ANN includes the at least one output biomarker value.

According to a third aspect, a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the second aspect.

Of course, the features recited in respect of each particular aspect can be readily applied to the other aspects.

The present invention relates to a mammalian health and wellbeing apparatus that implements a method utilising a combination of technologies and systems to establish a non-invasive biomarker value measurement.

More particularly the apparatus permits a measurement of mammalian homeostasis, and more particularly a biomarker measure of human body regulatory mechanisms.

Homeostasis is supported by two main functions: the autonomic nervous system and endothelial functions. The autonomic nervous system (ANS) is an extensive nervous network whose main role is to regulate the internal environment and body functions by controlling homeostasis which includes haemodynamics, blood pressure, heart rate, blood glucose level, sweating and visceral functions (Kalopita, Liatis et al. 2014). The ANS acts through a balance of stimulation or inhibition of two main components—the sympathetic and parasympathetic nervous systems. Sympathetic and parasympathetic branches act via neurotransmitters and receptors activation.

Endothelial function is related to the ability of the blood vessels to dilate or constrict when necessary. Endothelial dysfunction can be characterised as reduced bio-availability of Nitric Oxide (NO), which plays many roles in maintaining vascular health. Hence, endothelial dysfunction is defined as an impairment of endothelium dependent vasodilation. Endothelial dysfunction gives rise to vascular restriction or arterial stiffness which can be measured by a number of techniques including flow mediated dilatation using laser Doppler or ultrasound probes.

A method for monitoring cardiovascular events and peripheral circulation is through localised reflective video analysis (LRVA). LRVA uses reflected red light to measure relative blood volume in peripheral mammalian tissue such as the fingertip, toe, ear lobe or inner ear. LRVA waveforms are characteristics of blood movement in cutaneous vessels and can be used to identify synchronous depolarization of cardiovascular tissue. The fundamental frequency of the LRVA waveform, typically around 1 Hz representing a heart rate of 60 bpm. Lower frequency components such as respiratory, thermoregulatory and sympathetic nervous system effects are also contained within the LRVA signal. Arterial stiffness, indicative of endothelial dysfunction, may also be measurable from calculations made using the LRVA waveform analysis.

The apparatus/method of the present invention relates to the non-invasive measurement of a biomarker value. For example, a measurement of a blood glucose level in a human subject.

The subject may be a healthy individuals, or an individual that exhibits on-set of or near onset of Type II Diabetes Myelitis and individuals diagnosed with Type I Diabetes Myelitis. The invention is a means of providing simple tests to identify problems which would not normally result in the need for clinical diagnosis and in this respect are part of the wellness assessment of individuals.

For many subjects, it may be inappropriate to undertake ambulatory time-of-day blood glucose level measurements to determine blood glucose levels. The present invention may be particularly useful for subjects not previously diagnosed with the symptoms of Type II diabetes, for example. The present invention allows for a cost-effective biomarker value measurement platform installed on a cell-phone or other such mobile device, or installed on a remote server system to perform rapid biomarker value measurement.

The data so recorded may also be remotely accessed by the subject or a competent person, such as a physician, to review the biomarker value measurements.

Summary of the figures

So that the invention may be more readily understood, and so that further features thereof may be appreciated, embodiments of the invention will now be described by way of example with reference to the accompanying drawings in which:

Figure 1 is a schematic view of a mobile device according to an embodiment of the present invention;

Figure 2 is a schematic view of the steps of involved in deriving input parameters for an ANN in accordance with the present invention;

Figure 3A illustrates demonstrates the selection of a target region;

Figure 3B also illustrates demonstrates the selection of a target region;

Figure 4 illustrates a heartbeat trace;

Figure 5 illustrates a super pulse;

Figure 6 illustrates an ANN in accordance with the present invention;

Figure 7 illustrates another ANN in accordance with the present invention;

Figure 8 illustrates the efficacy of the present invention for a first biomarker;

Figure 9 also illustrates the efficacy of the present invention for the first biomarker;

Figure 10 illustrates the efficacy of the present invention for a second biomarker, and;

Figure 11 also illustrates the efficacy of the present invention for the second biomarker.

Detailed description of the invention

Turning now to consider Figure 1 in more detail, a schematic illustration of a mobile device 1 according to an embodiment of the present invention is shown. The mobile device 1 includes a camera 2, storage means 3, an analysis module 4, and a memory module 5.

The mobile device 1 may be a mobile phone, a smartphone, a smart watch, a wearable device such as glasses or contact lens, a computer peripheral (which may be configured for wired/wireless connection to a computer), a tablet, a computer or any other suitable mobile device. The mobile device 1 may be a dedicated biomarker value measurement device. For example, the mobile device may be a blood glucose monitor, i.e. a dedicated device for monitoring blood glucose level.

The camera 2 is capable of recording video into a video file. The video file is a continuous sequence of images of a scene that has been viewed by the camera 2. The camera 2 may create video files, which may be stored on the mobile device 1 in the storage means 3. The storage means may be RAM or a hard drive, for example. The video file may be sent from the mobile device 1 to a remote server.

The camera 2 may have a frame rate of greater than 24 Hz, preferably in the range 24 to 60 Hz, more preferably in the range of 30 Hz to 60 Hz. The mobile device may be configured to limit the frame rate to a maximum of 60Hz. The frame rate used is a compromise between data rate and noise level. A higher frame rate leads to higher data rate, which of course the mobile device must be able to handle (perhaps temporarily). The higher data rate gives more data to analyse at a better resolution, however the noise also increases within each frame for shorter frames (higher frame rate).

The camera 2 may create video files having a so-called Red-Green-Blue (“RGB”) three-channel output. The video files may be formed from a sequence of images, each image being formed from an array of pixel values. Each pixel value may be an RGB value representing the colour of the respective pixel in that frame. Other video formats may be used, for example HVC format. The videos may be converted from one format to another, for example from RGB to HVC format.

The storage means 3 may be hard drive or a RAM module, for example. The video files may be temporarily stored on the storage means 3. From the storage means 3, the video files are accessible for processing by the analysis module 4 (see below).

The analysis module 4 is configured to perform the analysis of a video file in order to calculate the value of a biomarker for the subject appearing in the video. The computer instructions that dictate the operation of the analysis module 4 may be stored in the memory module 5. The instructions may be in the form of an application or “app”, which can be loaded on to the mobile device 1. Such instructions form an embodiment of the present invention.

The camera 2 records a video of an area of skin of a subject. The video forms a sequence of frames, having a particular frame rate. For example, the frame rate may be 30 frames-persecond (“fps”)), which may be typical for cameras fitted to mobile telephones.

The camera 2 may be a front or back camera of a smartphone or device, for example. The camera may be a stationary mounted video camera.

A video is thus available for processing. The video shows an area of skin of a subject, for example an earlobe or a fingertip.

The video may be obtained directly from the camera 2 generally immediately preceding the the processing described herein. Alternatively, the video may be previously recorded, either by the mobile device, or via an entirely unrelated and/or distinct device. The availability of the video or the relevant data derived from the video (e.g. the heartbeat trace - see below) is what is required for subsequent processing to measure the biomarker value.

A series of steps may then be performed on the video and downstream data derived therefrom. These steps are illustrated in Figure 2. The arrow 6 is intended to illustrate the general direction of processing. However, the order of the steps illustrated in Figure 2 should not be considered limiting.

In general, the steps are:

• a target area identification step 7;

• an intensity calculation step 8;

a frame intensity calculation step 9;

• a heart rate/breathing rate determination step 10;

• a noise removal step 11;

• a heartbeat profile combination step 12, and;

• a super-pulse formation step 13.

All types of camera sensor create a different distortion on the images they produce. This is primarily caused by the geometry of the camera sensor and lens(es). For camera sensors, particularly those in mobile telephones, it has been found that the sensitivity to light across the focal plane of the optical sensor is not flat. In other words, there is variation in the responsiveness of the sensor across the focal plane to given intensity of incident light. It is therefore advantageous to use pixel data from an area of the sensor in which this distortion imposed by the camera sensor is at a minimum.

Returning to Figure 2, the first step is a target area identification step 7. During the target area identification step 7, a target area of the frames of the video is identified for further processing. The target area may be a sub-area of a total frame area of the video.

The target area contains a plurality of target pixels. The number of target pixels is lower than the total number of pixels per frame of the video. The target area may have a pre-determined shape and size, but the location of the target area within the frames is determined as the area having the lowest distortion. For example, the target area may be the area of the total frame of 100 x 100 pixels that has the lowest distortion. The target area may be defined as having alternative predetermined shapes and sizes.

The target area may be defined as a zone within the frame of the video where the change in brightness is the greatest as measured by the first derivative of the average brightness of the pixels in the zone. A plurality of test zones may be assessed, where the plurality of test zones each have a different location within the frame. For example, the change in brightness for each test zone may be calculated, and the test zone having the greatest change is brightness may be selected as the target area.

The selection of the target area may be performed during a pre-assessment or calibration period.

The target area may also be defined as a region without colour saturation in either the RGB or HVC spectra.

For example, Figure 3A shows a plot of signal response across the area of a typical camera sensor of a mobile device. The x- and y-axes represent position across a camera sensor. The of responsiveness of the sensor corresponds to the curved line. It will of course be noted that position across the camera sensor is analogous to position across a video frame derived from that camera sensor.

It is apparent that the sensitivity is both higher and flatter across the centre of the sensor. The box 17, for example, signifies a selection of the target area within the camera sensor/video frame.

Figure 3B illustrates another aspect of camera sensitivity. The lines on the plot each indicate the average brightness of pixels within a respective test zone). These lines generally correspond to heartbeat traces. The y-axis indicates position of the respective test zone within the camera sensor/frame in one dimension. The x-axis corresponds to time (sample number). Again, it is clear that there is distortion imparted to the heartbeat trace as a function of position across the sensor. The target area may be selected on the basis of the trace exhibiting minimum of distortion.

The target area is used in the measurement of the video but may be re-assessed for each new video.

In the intensity calculation step 8, for at least each of the target pixels in the target area, an intensity value is calculated. The intensity value may be calculated for every pixel in the frame before selection of the target pixels. The intensity for each pixel may be calculated based on a weighted sum of the R, G, and B values for the respective pixel.

In the frame intensity calculation step 9, a single intensity value for each target area in each frame is calculated. The frame intensity for a particular frame may be the average of the intensity values of the target pixels in the target area of that particular frame. An example plot of frame intensity (y-axis, arbitrary units) as a function of time (x-axis, seconds) is illustrated in Figure 4. Intensity as a function of time corresponds to a heartbeat trace. The heartbeat profiles are apparent as intensity fluctuations (the y-axis) as a function of time (the x-axis). Heartbeat profiles are sequentially present in the heartbeat trace of Figure 4.

For example, a proof of concept data collection trial was undertaken to demonstrate that a finger and facial LRVA using the red, green and blue colour scale was used to measure the characteristics of the systolic and diastolic changes in the video intensity. In this case, the video signal was converted into a frame intensity value as a simple average of the pixel colour values.

The computations and memory required to complete the LRVA of a Super-Pulse may not be compatible with some mobile devices. As a result the inventors disclose a system in which the LRVA analysis may be performed off-device, for example on a remote server.

In the heart rate/breathing rate determination step 10, the heart rate is determined by performing a Fourier transform of the heartbeat trace in the time domain (for example, using a “fast Fourier transform” (FFT) algorithm). The resultant power spectrum in the frequency domain can be analysed to identify the highest peak, which may correspond to the heartbeat rate in the heartbeat trace.

Similarly, the breathing rate of the subject may also be identified in the frequency domain power spectrum, and recorded.

Advantageously, the video frame rate is greater than or equal 30 frames-per-second (fps). The total length in time of the video image sequence used for determining the heart rate and respiration rate may be less than 30 seconds, for example.

A measurement of the erraticism of the heart rate, by contrast, may be derived from a video having a greater length. For example, a video having a length of greater than 60 seconds, in order to capture the desired signal, for example.

The erraticism of the heart rate is a measure of the variability of the heart rate. Very low heart rate variability (i.e. low erraticism) may be a marker of stress (or “burnout”) where the subject enters an unresponsive state. High heart rate variability (i.e. high erraticism) may be a characteristic of excess sympathetic nervous system response, which may ultimately lead to heart arrhythmia.

For example, the heart and respiration rate sequence is preferably half the length (in time) of the heart rate variability sequence with the heart and respiration rate signal analysis being performed at least twice every time the heart rate variability is analysed.

It may be desirable to minimise the total length of time of the video to minimise continual variability resulting from undesirable artefacts embedded in the signal (i.e. noise), such as those arising from movement and the micro-changes on positioning of the camera that records the video. Noise in the heartbeat trace may also arise from subject movement, source light flicker and video light loss, for example.

The noise in the heartbeat trace could be physiological in origin, or non-physiological in origin. Reduction and/or removal of such noise happens in the noise removal step 11.

In order to eliminate or reduce noise that is non-physiological in origin (for example, signal artefacts resultant from motion between the subject and the camera during the recording of the video) it may be advantageous to apply signal filtering to the heartbeat trace. Typical timeseries filtering techniques may be applied. One such example is identifying a portion of the power spectrum in the frequency domain as corresponding to a noise component, and removing that noise component in the frequency domain. An inverse Fourier transform is then performed to arrive at a filtered heartbeat trace with the noise component removed. By applying such heartbeat trace noise removal, and thereby removing unwanted noise components of the heartbeat trace, a more accurate biomarker value may ultimately be calculated.

Noise elimination/reduction can alternatively or additionally be achieved by reconstructing a “noise base signal” by removing the heart rate and respiration rate frequency peaks from the Fourier transform of the heartbeat trace, and then reconstructing the noise signal in the time domain by performing an inverse Fourier transform. The noise signal in the time domain can then be removed from the original heartbeat trace to provide an improved heartbeat trace with reduced noise.

In another potential method of noise reduction, a stabilisation portion of the video at the start of a particular video may be excluded from further processing. The stabilisation portion may be used to allow stabilisation of the video. After stabilisation of the video, the stabilisation portion of the video may be discarded. This may be done because, when the video is initially switched on, the video signal may be erratic. After a stabilisation period, the video signal may settle down or stabilise. It has been identified that the stabilisation period may be approximately 5 seconds for example. Thus, the stabilisation portion that is discarded may include the initial portion of the video having a length equal to the stabilisation period. Excluding the stabilising portion of the video may reduce noise.

In another potential method of noise reduction, the mobile device may be configured to use measurements from an accelerometer to restart the processing if motion artefacts exceed a predetermined threshold.

In addition, noise components in the heartbeat trace can also be caused by physiological changes in the subject that occur during the period that the video is recorded. Such physiological changes can manifest as variations in the heartbeat trace. Such noise components may not have their origins in noise derived from motion artefacts or harmonics. A pulse trace containing physiological-origin noise of this type may result in lower accuracy biomarker values being measured therefrom. This kind of noise may be mitigated in a heartbeat profile combination step 12 and the super pulse step 13.

When the heartbeat rate has been identified, a next step is to improve the signal to noise ratio of the heartbeat profiles in the heartbeat trace. One way to achieve this is to combine the individual heartbeat profiles with one another. This happens during the heartbeat profile combination step 12.

Using the heartbeat rate, from which a heartbeat period can be calculated, the heartbeat trace can be separated temporally into a plurality of heartbeat profiles each having a length of one heartbeat profile (see Figure 4). Each heartbeat profile corresponds to a single heartbeat period, and thus includes a single heartbeat.

The heartbeat profiles may be combined to form a single combined heartbeat profile. An example method of combining the heartbeat profiles is to add the profiles together sample by sample. Temporal alignment between the individual heartbeat profiles before adding of aligned samples may be achieved based on alignment of a particular feature or features of the shape of the individual heartbeat profiles. This allows for more accurate alignment, and thus improved SNR of the combined heartbeat profile relative to the individual heartbeat profiles.

The combined heartbeat profile intensity values may be baselined to zero, or some other value. The intensity values in the combined heartbeat profile may be scaled.

Once the combined heartbeat profile has been formed, a “super pulse” is formed during a super-pulse formation step 13. The super pulse is a functional representation of the combined heartbeat profile.

One such functional form is a combination of a plurality of Gaussian functions. The combination may be a summation of the plurality of Gaussian functions. The fitting of the Gaussian functions to the combined heartbeat profile may be achieved by applying a decision tree. The decision tree therefore effectively synthesises the combined heartbeat profile.

For example, synthesising nine Gaussian functions, and fitting those to the combined heartbeat profile, results in an advantageous representation for a super pulse. Each Gaussian function can be defined by three parameters - the central value, the width (e.g. the full-width half-maximum), and the peak height. For example, three Gaussian functions can be described by 9 parameters; five Gaussian functions by 15 parameters; and nine Gaussian functions by 27 parameters, for example.

Figure 5 shows a super pulse, formed from the summation of five Gaussian functions. The heartbeat profiles that were used to form the combined heartbeat profile are those shown in Figure 4. The combined heartbeat profile was used to form the super pulse shown in Figure 5. Each Gaussian function may correspond to a feature in the combined heartbeat profile.

These parameters of the Gaussian functions are determined by using a non-linear optimisation method whereby an objective “cost” function represented by the minimisation of the sum of the squares of the difference between the combined heartbeat profile and a test Gaussian solution is calculated. When the parameters of the test Gaussian solution are determined such that the objective cost function has a minimum value (for example, <0.0001) then the Gaussian parameters for the super pulse may be determined. The Gaussian parameters for the super pulse may be contained in a parameters vector. The values in the parameters vector may be converted into an absolute index. The absolute index is a numeric value that uniquely characterises the individual subject, and may change with medication intake and lifestyle. The frame rate of the video file ultimately dictates the original sampling rate of the heartbeat trace and of the combined heartbeat profile.

The functional form of the super pulse is known (by the plurality of Gaussian functions and the corresponding parameters, for example). Accordingly, it is possible to increase the sampling rate of the super pulse, relative to the original sampling rate, to a higher sampling rate during the interpolation step 14. The higher sampling rate allows for a higher temporal resolution in the super pulse than the temporal resolution of the combined heartbeat profile. Accordingly, temporally smaller features and shorter time periods within the super pulse can be identified and utilised.

For example, the original sampling rate may be 30 Hertz and the higher sampling rate may be 1000 Hertz. The samples that are created in the super pulse in the process of increasing the sampling rate (i.e. the samples added between the actual samples from the video) may be interpolated based on the value of the Gaussian function(s) at that point in the super pulse.

The combined heartbeat profile is similar in utility to an individual’s fingerprint. In other words, the super pulse is characteristic of an individual. The mobile device may be configured to verify the identity of the individual using the mobile device by comparison of the super pulse to a reference super pulse for a user. Furthermore, the characteristics (for example, the shapes, sizes, and dimensions) of the combined heartbeat profile and its components depend on the value of a great many biomarker values of the subject.

Identifying a functional form that accurately describes the characteristics of the combined heartbeat profile allows for accurate measurements of the characteristics of the combined heartbeat profile and its components via analysis of the super pulse. Such measurements can then be used the calculation of a biomarker value or values.

It is also noted that the characteristics of the combined heartbeat profile, and by extension the characteristics of the super pulse, may be modified by the action of a medication or functional foods, vitamins, or foods in a changed diet and exercise routine and furthermore can be used to identify the result of consuming food on a biomarker value. Furthermore, the characteristics can be shown to vary with the subject’s circadian rhythm.

Where the super pulse is represented using a plurality of Gaussian functions, the parameters that characterise the super pulse may be recorded as a subject vector. The absolute value of the subject vector may provide a convenient and reproducible single numeric super pulse value.

The super pulse may also allow acute and chronic changes on vascular health to be recorded and related to other factors such as pulse wave velocity, variable heart rate, stiffness index and flow mediated dilatation and stress and mental fatigue indexes.

The super pulse also contains valuable information about the orthogonal orientation of red and white blood cells with respect to time. The orthogonal orientation of the blood cells defines the velocity of travel of the cells.

For example, it may be possible to identify the point of the systole in the super pulse. The time of the systole may form an input to the ANN (see below). The systole point may be timestamped, using the super pulse of the present invention.

The super pulse may also be analysed in such a way as to highlight the change in alertness of the subject. This may be achieved, for example, by computing the areas under the super pulse for that part of the super pulse representing the systole and the diastole. Such areas may form inputs to the ANN (see below). The ratio of these areas may change as a direct result of a subject undertaking a task where the use of a medication, or functional foods, vitamins, or foods in a changed diet, or exercise. The ratio of the areas may form an input to the ANN (see below). Such example activities may directly affect this ratio. In this way, the subject’s response to a particular intervention can be measured and presented as an alertness index. An alertness index may be calculated by a precursor ANN, and input to the primary ANN.

In a further embodiment of the invention, the mobile device may also be used to record the medications, functional foods, vitamins, or foods consumed on a daily basis by scanning a barcode. In this way, a clear record of consumption can be made without recourse to “after the event food consumption questionnaires.

When all the aforementioned aspects are combined it is possible to identify the correlation of the power spectral density of the recorded information and utilise that information to correlate with measured insulin resistance. The power spectral density of the video provides another set of parameters, for example parameters describing heartrate variability (erraticism).

According to the present invention, the mobile device is able to non-invasively calculate a biomarker value of a subject. Examples of such a biomarker are insulin resistance, blood glucose concentration, systolic and diastolic blood pressure and core or skin surface temperature, red blood cell count, histamine levels and/or immunoglobulins for example.

Equally, the present invention may be configured to calculate a plurality of biomarker values.

By way of example, the following discussion relates to blood glucose level as the biomarker.

In the present invention, an Artificial Neural Network (“ANN”) module is implemented in the analysis step 15 (see Figure 2). The analysis step is implemented in the analysis module 4 of the mobile device 1. Alternatively, the analysis step 15 may be implemented on a remote server, remote from the mobile device 1.

An ANN is a machine learning technique. In general, an ANN is based upon a plurality of interconnected (in a mathematical sense) computational units (commonly referred to as “neurons”). Generally, the neurons are connected in layers, and signals travel from the first (input) layer, to the last (output) layer. The variables (incoming and outgoing) and the state of each neuron is a real number, typically chosen to be between 0 and 1.

Figure 6 illustrates an example of a feedforward artificial neural network (FNN). An FNN is a category of ANN. The FNN includes an input layer 22 and output layer 23. Between the input layer 22 and the output layer 23 is a single hidden layer 24. The input layer 22 includes input neurons 22A; the hidden layer 24 includes hidden neurons 24A, and; the output layer 23 includes output neurons 23A. There are input neural connections 25 between the input neurons 22A and the hidden neurons 24A. There are output neural connections 26 between the hidden neurons 24A and the output neurons 23A. Data flows one way from the input layer 22, through the hidden layer 24, to the output layer 23. In an FNN, the data only flows this way. An FNN (and an ANN, in general) may have more than one hidden layer 24.

The input to the input layer 22 may be an input vector. The input vector may be a plurality of numeric or Boolean values, each respective value in the input vector being used as an input to a corresponding input neuron 22A in the input layer 22. The output from the output layer 23 may be an output vector. The output vector may be a plurality of output values, each respective output value in the output vector being output from a corresponding output neuron 23A in the output layer 23. The length of input vector may be different from the length of the output vector. The length of input vector may be equal to the length of the output vector.

In general, each neuron 22A, 23A, 24A calculates a weighted sum of all of its inputs. The neuron may also apply a bias. The weighted sum is used as the argument of an activation 15 function for the respective neuron. The output of the activation function is used as the output of the neuron. The output of the neuron may be fed into one or more downstream (i.e. subsequent layer) neurons through one or more outbound neuron connections from the neuron.

Using an ANN may allow for nonlinear models, non-Gaussian belief distributions and unknown or only partly known modelling equations. Missing knowledge about the system is extracted during the training process of the ANN.

ANNs have been designed and implemented to solve regression problems. Regression involves estimating a mathematical relationship between variables. For example, regression may correspond to the process of fitting continuous curves onto noisy data - thus estimating the mathematical form of a relationship between the variable as described by the noisy data.

By using ANNs, relationships between variables can be described that are highly nonlinear and may be of arbitrary complexity. The complexity of the relationship that the ANN can describe may be limited by the choice of hyper-parameters of the ANN. The hyper-parameters of an ANN correspond to the descriptors of the ANN itself, rather than the underlying data. For example, the hyper-parameters may include the number of neurons in the ANN and the number of layers into which those neurons are arranged and the connections therebetween.

The ANN module of the present invention may include a single ANN similar to that shown in Figure 6, or may include multiple such ANNs. In the case of multiple ANNs, the ANN module may include at least one primary ANN, and at least one precursor ANN, in which an output of the precursor ANN forms an input to the primary ANN. The ANN module may include a primary ANN and a plurality of precursor ANN, in which an output of each precursor ANN forms part of the input to the primary ANN.

Figure 7 shows a schematic of ANN module 30 in accordance with the present invention.

The ANN module 30 includes a primary ANN 31 (in this case, an FNN) and two precursor ANNs 32A, 32B. The ANN module uses input 33 to calculate output 34. The data flows from input 33 towards output 34 (i.e. generally from left to right in Figure 7).

The input 33 may include, for example only, any combination of the following:

• direct super pulse values, directly describing the super pulse, including:

o at least one parameter defining the functional form of the super pulse (for example, the Gaussian parameters);

• indirect super pulse values, derived from the super pulse, including:

o a relative time period between two points of the super pulse (for example, the time between systolic and diastolic peaks, which may be defined as the phase difference between systolic and diastolic peaks);

o a magnitude of a point on the super pulse o a ratio of the magnitudes of two points of the super pulse;

o a ratio of the areas beneath components of the super pulse (for example the area corresponding to the systole and the area corresponds to the diastole);

o depth of a dicrotic notch • measured video values, derived or obtained without direct reference to the super pulse but ultimately derived from the video, including:

o heart rate;

o breathing rate;

o heart rate erraticism.

• extrinsic values that describe the subject, including:

o Chronological age o Health status (e.g. diabetic status) o Height o Weight o Gender o Body Mass Index (“BMI”) o Country of residence o Nationality o Ethnicity o Exercise routine o Medication consumed o Functional foods consumed o Vitamins consumed o Sleep periods

The input 33 may form a vector of such values. Some of these values may be determined via a precursor ANN before input to a primary ANN (see below).

The output 34 may include one or more biomarker values. At least one biomarker values may be cardiovascular biomarker. Alternatively or additionally, at least one biomarker value may be haemal biomarker. The one or more biomarker values may include, by way of example only:

• Blood glucose level;

• Blood pressure (systolic and diastolic);

• Core and/or skin temperature;

• Heart rate;

• Vascular age (which may be an indicator of heart health, and not necessarily equal to chronological age);

• Arterial stiffness index;

• Augmentation index;

• Left ventricle eject time;

• Flow mediated dilatation;

• Blood gas saturation;

• Insulin resistance;

• Stress levels;

• Mental fatigue levels;

• Histamine levels

Immunoglobulin levels;

Hormone levels

Red blood cell count.

White blood cell count

Blood clotting characteristics (FVIII & FIV, for example)

ALT;

Alkaline phosphatase level;

Bilirubin (Total);

Calcium level;

Carbon dioxide level;

Chloride level;

Creatinine level;

Glucose level;

Potassium level;

Phosphate level;

Sodium level;

Urea level;

Nitrogen (BUN) level

A Biochemistry C Profile, including for example, beta 2 microglobulin, BNP, Serum Electrophoresis (EPS), FSH, IGF-1, Immunofixation - serum or urine, Immunoglobulins (A,G,M), Paraprotein measurement (Densitometry), Progesterone, PSA, Prolactin, Testosterone, TGB, Thyroglobulin/TG auto Ab, Total T3, Transferrin/TIBC, TSH.

The output list above may include values that are measured accurately and used in the training and validation of the ANN module (see below).

The precursor ANN 32A, 32B are each used to determine one or more precursor output values. A precursor ANN may be useful where it is known, or suspected, that a particular value may be useful to the predictive capability of the primary ANN 31.

For example, a particular precursor ANN may take all or part of the input 33 as an input. The desired precursor output values 32C, 32D from the respective precursor ANN 32A, 32B, may be defined as, for example:

• Breathing volume. This may use as input to the precursor ANN 32A, 32B, for example, breathing rate derived from the heart beat trace and parameters describing the functional form of the super pulse;

• Systolic and diastolic blood pressure (which may be output from the same predcursor ANN 32A, 32B). This may use as input to the precursor ANN 32A, 32B, for example, parameters describing the functional form of the super pulse.

• Core temperature. This may form an input to the precursor ANN 32A, 32B, for example, in combination with heart rate parameters.

Any particular input 33 to the ANN module 30 as a whole may form an input to a precursor ANN 32A, 32B and/or an input to the primary ANN 31. Multiple precursor ANNs 32A, 32B may share common inputs or have overlapping inputs. Multiple precursor ANNs 32A, 32B may have identical inputs, but different precursor outputs. A precursor output 32C, 32D from a precursor ANN 32A, 32B may form an input to the primary ANN 31.

It is noted that an ANN module not including the precursor-primary ANN structure could, may, using the same inputs, alternatively derive corresponding relationships as those represented by the precursor-primary ANN combination. Indeed, biomarker values can be effectively calculated in this way (i.e. with no precursor ANN).

However, it has been found that the predictive performance (e.g. accuracy) of the ANN module 30 in determining the biomarker in the output 34 is further improved by including at least one precursor ANN 32A, 32B. Each precursor ANN 32A, 32B is used to derive, as an input to the primary ANN 31, a precursor output value from the input 33, which is used as an input to the primary ANN 31. Effectively, the precursor ANN forces the ANN module to calculate a particular value that is considered an important variable in biomarker value calculation.

In a general sense, for an ANN to be able to calculate an output from a given input, where the ANN has not seen that particular input previously, the ANN must have been trained. That is, the ANN must be configured (“trained”) to have the desired predictive behaviour.

In very general terms, training means determining the weight and bias for each neuron. In general, training is achieved by using a training data set in which the input and the desired output is known - this is called supervised training with a labelled data set (see below). The weights and biases of the ANN are changed such that the input of the training data set is converted to the desired output of the training data set as closely as possible. The weights and biases may be optimised by minimising an error function (for example via error backpropagation).

A validation data set, which also includes inputs with desired outputs, may be used to verify and check the performance of the ANN. The validation data set is not used in training the ANN (i.e. determining the weights and biases of the ANN). In other words, the validation data set is separate from the training data set.

Producing the training and validation datasets usually requires “labelling” of each data set. That means that it has been determined that the output is what is desired from the corresponding input. Labelling may be time consuming. Training and validation sets are said to contain “matched pairs” of data. Each matched pair is a set of input data with a known or desired output.

For the present invention, the aim for the ANN module is to calculate a biomarker value from non-invasively measured input values. In order to train the ANN module to achieve that predictive power, the ANN module must be trained to implement a relationship between the non-invasively measured parameters and a desired measured biomarker value. The desired measured biomarker value may have been measured invasively. The ANN module in particular allows a biomarker value to be calculated from input parameters describing the super pulse or input parameters derived from analysis of the super pulse (and optionally other inputs listed above, for example).

The training data and validation data each comprises a plurality of matched pairs of data. Each matched pair includes a known output and a known input. The known input (e.g. the super pulse parameters) and the known output (i.e. the desired biomarker value) were measured at generally the same time. The biomarker value may have been measured using conventional methods. For example, a blood glucose level measurement could be made using a blood glucose measurement of a blood sample. The super pulse measurements that correspond to that biomarker value are measured generally simultaneously as the biomarker value is measured (or the time at which the sample is taken).

The matched pairs in the training and validation data are thus a set of clinical parameters of a plurality of subjects, a set of LVRA signals from the plurality of subjects, and a set of synchronised biomarker values from the same plurality of subjects. Other parameters may also be included, for example age, ethnicity, gender and disease status (i.e. extrinsic values).

In the case of blood glucose level, synchronised biomarker values can be derived from a glucometer or from whole blood values where the blood glucose value is determined using, by example, YSI 2900 analyser.

Any precursor ANNs present in the ANN module may be trained separately from the training of the primary ANN. The matched pairs for training the precursor ANN would be the particular inputs used by the precursor ANN. The known output would the particular desired precursor output value.

It may be advantageous to train the precursor ANNs separately from the primary ANN because training ANNs may be a time consuming/compute-cycle intensive process.

Of course, there may be an uncertainty in invasively or non-invasively measured biomarker values. This applies to the biomarker values used in the training data.

For example, in the case of glucometer readings of blood glucose level, the readings represent interstitial fluid readings taken from the dermal layer. Such readings are known to lag behind that of whole blood values in time and value. Surprisingly, the inventors have discovered that the variation in glucometer blood glucose synchronised measurements can be up to ±27% with a sample mean error of ±7% over the period of a postprandial test sequence. As a result, the inventors have discovered that variation in glucometer accuracy may reduce the potential accuracy of an ANN module trained using glucometer measured biomarker values.

To address this issue, the inventors have used “fuzzy clustering” of the glucometer readings. This technique has been implemented to create of a fuzzy cluster of desired biomarker values, which is determined by the error distribution of the glucometer-measured biomarker values used to train the network. For example, the method by which the biomarker value is measured may be known to have a measurement standard deviation. For each measurement biomarker value, two further biomarker values may be generated. For example, one fuzzy biomarker value at measured value plus 1 standard deviation, and one fuzzy biomarker value at measured value minus 1 standard deviation. Together the measured value and the fuzzy values form a fuzzy cluster. Each of the measured value and the further values use the same input. The number of matched pairs of for training and/or validation is thus increased by the use of fuzzy clustering.

In the case of “gold standard” devices such as the YSI 2900, blood glucose error distribution is far smaller, however the principal remains. In such gold standard devices, the mean error in 22 whole blood and plasma measured blood glucose level values may range from 1.8% to 2.5%, for example. As such, if the YSI 2900 were used to generate blood glucose level values for training, a fuzzy cluster with an error spread would range between 1.8% and 2.5% would result. Clearly, this is a smaller error spread that for glucometer measured blood glucose level values. Again, however, a fuzzy cluster may be used to define fuzzy biomarker output values and input values (i.e. matched pairs) for the training and validation data.

Thus, the training and validation data for the ANN module may include fuzzy biomarker output values and input values. The fuzzy biomarker output values reflect the uncertainty in the measurements of those biomarker values.

In this way, the inventors have shown that it is possible to determine accurately blood glucose, insulin resistance and blood pressure and other biomarker values for a user from non-invasive LVRA. Furthermore, the inventors have shown that the effect of medications, functional foods, vitamins, or foods consumed on a daily basis or exercise can be reflected in the baseline blood glucose levels and a general improvement in (reduction of) insulin resistance.

The aim of the ANN module is that is it can predict the output biomarker value(s) for input data that the ANN module has not seen before, and which has not been used as part of the training process.

If the ANN module did not produce generalised predictive behaviour, it would correspond to an “over-fitted” ANN. An over fitted ANN is one that can only accurately predict output data for input data that was used in the training data (i.e. input data that the ANN has seen before). An over fitted ANN would not accurately predict output data for input data that was not used in the training data. Clearly, an over fitted ANN is undesirable.

To demonstrate and measure the efficacy of the ANN module in the present invention, the predictive capability of the ANN module is assessed using validation data. The validation data is another set of matched pairs, similar to the training data. However, the matched pairs of the validation data are not included in the training data.

In an example of training of the ANN module of the present invention, a Levenberg-Marquardt backpropagation method was used to train the ANN module using a fuzzy training set. The predicted blood glucose measurements (i.e. output biomarker values) for a validation data set were compared to invasively measured blood glucose measurements in the validation set.

Figure 8 shows a Clarke error grid (CEG). In general, the CEG compares a scatterplot of values from a reference method and a new method under test. The CEG provides an illustration of how accurately the new method is able predict the values from the reference method.

In the example of Figure 8, the x-axis is blood glucose level in milligrams per decilitre measured using the reference method; the y-axis is blood glucose level in milligrams per decilitre measured using the new method.

The CEG is a consensus accuracy grid which displays regions assigned A to E where A is a region where maximum accuracy is required, and B, C, D and E are regions where less accuracy is satisfactory or accuracy is not important or unacceptable. The CEG for blood glucose level self-monitoring was constructed by using the expertise of a large panel of clinicians and is now in widespread use to evaluate the accuracy of blood glucose level measurements made by patients.

The CEG is split into five types of region (as above):

• Region A are those values within +/-15 % of the reference sensor, • Region B contains points that are outside of +/-15% but would not lead to inappropriate treatment, • Region C are those points leading to unnecessary treatment, • Region D are those points indicating a potentially dangerous failure to detect hypoglycaemia or hyperglycaemia, and • Region E are those points that would confuse treatment of hypoglycaemia for hyperglycaemia and vice versa.

Figure 8 also shows the results (the points on the CEG plot) of a method according to an embodiment of the present invention. Figure 8 demonstrates that the ANN module calculates the blood glucose level entirely within clinically acceptable standards for unseen, noninvasively measured inputs. The ANN module used to produce the results of Figure 8 does not use the fuzzy data approach to training described above.

Figure 9 is identical to Figure 8, except that the ANN module used to produce the results of Figure 9 does use the fuzzy data approach to training described above. Figure 9 demonstrates that the ANN module calculates the blood glucose level entirely within clinically acceptable standards for unseen, non-invasively measured inputs (e.g. validation data).

Figure 9 shows the improved results obtained when using an ANN module trained using fuzzy data points. A significant majority of the points can be seen in the Ά' classification range of the CEG. There is a notably larger spread amongst the results for type 1 diabetics, and this group also contains every data point outside the Ά' classification.

One method of testing upon unseen validation data is through cross-validation of a combined data set. This is a process completed over the course of a number of training rounds. In each training round, the combined data is partitioned into two sets: the first is the training data set, whilst the second is the testing data set. In a first training round, the ANN is trained using the training data set, before being tested on the testing data set. In a second, subsequent, training round, another partition of the combined dataset is made and the training process is repeated. This allows for a wide range of data subsets to be used whilst retaining the properties of unseen data. Analysing the errors in predictive capability of the ANN module from each training round, across all rounds, provides information on the generalisability of the approach.

A particular form of cross validation that may be used in training the ANN module of the present invention is so-called “leave-one-out cross validation” (“LOOCV”). Here, each testing round uses a single data point as the testing data, and the rest of the data is used for training data. This allows every single data point to be tested as unseen data, which is advantageous when there is only a limited amount of data available.

An alternative form of cross validation that may be used in training the ANN of the present invention is so-called “leave-p-out cross validation” (“LpOCV”). Here, each testing round uses a plurality, p, of data points as the testing data, and the rest of the data is used for training data.

The ANN module used to generate the data shown in Figure 9 was trained by performing LOOCV using a data set derived from healthy, Type II and Type I subjects using the ANN described above, and the Levenberg-Marquardt strategy of back-propagation.

The data clearly shows that the system of the present invention is capable of producing 96.4% of the results within the no-clinical risk region of the CEG.

Figure 10 illustrates the efficacy of an embodiment of the present invention in which the output biomarker is blood pressure. In particular, Figure 10 is a plot of blood pressure measured according to the present invention using an ANN method described above (on the x-axis) against blood pressure measured using a conventional blood pressure measurement cuff (on the y-axis). The solid circles represent the training data. The open circles represent the results for a test user. The line represents a 1 to 1 linear relationship between the blood pressure calculated according to the present invention and blood pressure measured using a cuff.

Figure 10 demonstrates that the ANN method for calculating a biomarker value (the blood pressure, in this instance) produces biomarker values that accurately reflect the blood pressure values measured using conventional cuff techniques.

Figure 11 illustrates a Bland Altman plot also illustrating the efficacy of an embodiment of the present invention in which the output biomarker value is blood pressure. The x-axis of the Bland Altman plot of Figure 11 is the mean of a Mean Arterial blood pressure measured according to the present invention using an ANN method and a blood pressure measured using a conventional blood pressure measurement cuff. The y-axis of the Bland Altman plot of Figure 11 is the difference (as a percentage) between Mean Arterial Pressure measured according to the present invention using an ANN method and Mean Arterial Pressure measured using a conventional blood pressure measurement cuff.

Mean Arterial Blood Pressure may be defined as Diastolic Pressure + 1/3*(Systolic-Diastolic). The difference between Systolic and Diastolic pressures is called the pulse pressure.

Figure 11 demonstrates that the ANN method for measuring the blood pressure biomarker produces low bias (the ANN biomarker measurements are close to zero on the y-axis) and the mean error is low (the ANN biomarker measurements are clustered between +/- 1.96 sigma, which corresponds to a 95% confidence interval.

In a first potential use of an embodiment of the present invention, subjects who have a need to manage their diet, or who consume medications such as Metformin in the case of Type II diabetics, or functional foods, vitamins, or low carbohydrate foods, in a changed diet regime as prescribed, may permit the subject’s data to be returned to a central registry. In this way, a population-related benefit of the particular medication or functional foods, vitamins, or foods in a changed diet can be demonstrated in a pseudo parallel-use environment such that many thousands of participants’ data can be assessed. This type of analysis allows the recording of the change in the health of a typical or average user classified by a number of selected parameters such as age, gender, BMI, country, ethnicity, genetics, exercise routine and medication consumed, such that the benefit of the medication or functional foods, vitamins, or foods in a changed diet can be unequivocally demonstrated. This embodiment is particularly valuable for assessing the change in or improvement in subjects on the borderline of being diagnosed with type II diabetes.

In a second potential use of an embodiment of the present invention, the subject may be an employee of a corporation or organisation in need of medical or occupational health management. For example, as may be the case with sedentary workers or workers in locations where dust or solvents are prevalent, or particularly for night shift workers be they in general industry or service industries or hospital workers, or military personnel including submariners and astronauts. In such cases, the subject (e.g. user of mobile device) is an employee, who is advised to undertake a preferred routine such as exercise or to change their diet to include healthy foods, supplements, vitamins and/or functional foods.

The analysis of the data from the mobile device allows the recording of the change in the health of the user classified by, and linked to, their medical records including data for a number of selected parameters such as age, gender, BMI, country, ethnicity, exercise routine, medication or functional foods, vitamins, or foods in a changed diet, sleep period, heart rate and its variability (by way of example, not limited here). In this was the benefit of the regimen can be unequivocally demonstrated and actions advised to improve and demonstrate the improvements in health of the subject. This embodiment is particularly useful in cohorts where the user is pre-disposed to weight gain or lack of exercise.

In a third potential use of an embodiment of the present invention, the subject using a mobile device consumes a product which reduces their increase in insulin resistance, as is the case with people experiencing mild cognitive impairment. In this aspect the mobile device measures appropriate physiological parameters (biomarkers), and the mobile device provides an onscreen cognitive test series, specifically related to executive function and memory associated with increase brain glucose or ketone bodies. The subject permits their data to be returned to a central registry such that the population-related benefits of the product which can be medication or a medical food or a food for special medical purpose or functional foods, vitamins, or foods in a changed diet can be demonstrated in a pseudo parallel clinical trial. In this way the costs of acquiring the long-term efficacy data is reduced by many orders of magnitude. In this context, cognitive confusion or delirium is often associated with urinary tract infections and this invention is particularly useful at eliminating those individuals suffering in this way from the overall cohort involved in the cognitive tests.

In a fourth potential use of an embodiment of the present invention, the mobile device is a Bluetooth or similar finger-tip device with an associated product. For example, the associated product may be a blood pressure reducing medication. A daily dose of the medication is combined with the mobile device (which may be an application on a smartphone) which are access enabled with the associated product such that the effectiveness can be monitored remotely by a clinician. In this way, the effectiveness of the medication can be determined by both the patient, the clinician, and the manufacturer of the medication. In addition, the manufacturer gains valuable access to the users’ characteristics, irrespective of the point of prescription.

The features disclosed in the foregoing description, or in the following claims, or in the accompanying drawings, expressed in their specific forms or in terms of a means for performing the disclosed function, or a method or process for obtaining the disclosed results, as appropriate, may, separately, or in any combination of such features, be utilised for realising the invention in diverse forms thereof.

While the invention has been described in conjunction with the exemplary embodiments described above, many equivalent modifications and variations will be apparent to those skilled in the art when given this disclosure. Accordingly, the exemplary embodiments of the invention set forth above are considered to be illustrative and not limiting. Various changes to the described embodiments may be made without departing from the spirit and scope of the invention.

For the avoidance of any doubt, any theoretical explanations provided herein are provided for the purposes of improving the understanding of a reader. The inventors do not wish to be bound by any of these theoretical explanations.

Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

Throughout this specification, including the claims which follow, unless the context requires otherwise, the word “comprise” and “include”, and variations such as “comprises”, “comprising”, and “including” will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by the use of the antecedent “about,” it will be understood that the particular value forms another embodiment. The term “about” in relation to a numerical value is optional and means for example +/- 10%.

Claims (16)

CLAIMS:

1. An apparatus for calculating a biomarker value, comprising:

an analysis module configured to:

obtain a heartbeat trace of a subject;

determine at least one parameter describing the heartbeat trace;

calculate at least one output biomarker value using at least one Artificial Neural Network (“ANN”);

wherein an input of the ANN includes the at least one parameter, and an output of the ANN includes the at least one output biomarker value.

2. An apparatus according to claim 1, wherein the ANN includes an input layer having a plurality of input neurons, where the number of input neurons is equal to the number of parameters.

3. An apparatus according to claim 1 or claim 2, wherein the ANN includes an output layer having at least one output neuron, where the number of output neurons is equal to the number of output biomarker values.

4. An apparatus according to any preceding claim, wherein obtaining the heartbeat trace includes deriving the heartbeat trace from a video of the subject.

5. An apparatus according to claim 4, wherein obtaining the heartbeat trace includes measuring an intensity within a target region of each frame of the video, the intensity as a function of time forming the heartbeat trace.

6. An apparatus according to any preceding claim, wherein obtaining the heartbeat trace includes:

performing a Fourier transform of the heartbeat trace to identify the heartbeat frequency.

7. An apparatus according to any preceding claim, wherein obtaining the heartbeat trace includes measuring a noise signal in the heartbeat trace, and removing the noise signal from the heartbeat trace.

8. An apparatus according to any preceding claim, wherein the obtaining the heartbeat trace includes combining a plurality of individual heartbeat profiles in the heartbeat trace to form a combined heartbeat profile.

9. An apparatus according to any preceding claim, wherein a plurality of individual heartbeat profiles are temporally aligned using a feature of the plurality of the individual heartbeat profiles.

10. An apparatus according to any preceding claim, wherein the ANN includes a primary ANN and at least one precursor ANN.

11. An apparatus according to claim 10, wherein at least one of the input numerical values to the primary ANN is an output from one of the at least one precursor ANN.

12. An apparatus according to any preceding claim, wherein the input of the ANN includes at least one extrinsic value for the subject.

13. An apparatus according to any preceding claim, wherein determining at least one parameter describing the heartbeat trace includes determining a functional form of the super pulse, and wherein the input of the ANN includes at least one parameter of the functional form.

14. An apparatus according to claim 13, wherein the functional form includes a plurality of Gaussian functions.

15. A method of calculating a biomarker value, comprising:

obtaining a heartbeat trace of a subject;

determining at least one parameter describing the heartbeat trace;

calculating at least one output biomarker value using at least one Artificial Neural Network (“ANN”);

wherein an input of the ANN includes the at least one parameter, and an output of the ANN includes the at least one output biomarker value.

16. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of claim 15.